{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSDD数据集的audio classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip3 install -q librosa numpy matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install -q  tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 原始音频进行增广"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "import soundfile as sf\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "'''\n",
    "Function for applying following transforms on the wave files:\n",
    "1. Noise Addition using normal distribution of mean =0 and std =1\n",
    "2. Shifting Sound wave\n",
    "3. Time-stretching\n",
    "4. Pitch - shifting of a wave\n",
    "'''\n",
    "\n",
    "def audioDataTransforms(wav_file_path:str, tfms_folder_path:str) -> None:\n",
    "    fname = wav_file_path.split('.')[0]\n",
    "    wav, sr = librosa.load(os.path.join(tfms_folder_path, wav_file_path),sr=None)\n",
    "\n",
    "    #tfms 1: Noise addition:\n",
    "    wav_n = wav + 0.009*np.random.normal(0,1,len(wav))\n",
    "\n",
    "    #tfms 2: Shifting Sound wave:\n",
    "    wav_roll = np.roll(wav,int(sr/10))\n",
    "\n",
    "    #tfms 3: Time - stretching\n",
    "    factor = 0.4\n",
    "    wav_time_stch = librosa.effects.time_stretch(wav, rate=factor)\n",
    "\n",
    "    #tfms 4: Pitch - Shifting\n",
    "    wav_pitch_sf = librosa.effects.pitch_shift(y=wav,sr=sr,n_steps=-5)\n",
    "\n",
    "    # Write out audio as 24bit PCM WAV\n",
    "    sf.write(os.path.join(tfms_folder_path, f'{fname}_noise.wav'), wav_n, sr)\n",
    "    sf.write(os.path.join(tfms_folder_path, f'{fname}_sshifting.wav'), wav_roll, sr)\n",
    "    sf.write(os.path.join(tfms_folder_path, f'{fname}_stretch.wav'), wav_time_stch, sr)\n",
    "    sf.write(os.path.join(tfms_folder_path, f'{fname}_pshift.wav'), wav_pitch_sf, sr)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 音频变换成频谱图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "'''\n",
    "Function for converting all the waves files to image files.\n",
    "'''\n",
    "\n",
    "def wavesToSpecs(audio_folder, audio_name:str, spec_path:str):\n",
    "  \n",
    "  fname = audio_name.split('.')[0]\n",
    "\n",
    "  samples, sample_rate = librosa.load(os.path.join(audio_folder, audio_name),sr=None)\n",
    "\n",
    "  fig, ax = plt.subplots(figsize=(5,5))\n",
    "  ax.set_axis_off()\n",
    "  ax.specgram(samples,Fs=2);\n",
    "  fig.savefig(os.path.join(spec_path, f'{fname}.jpg'),bbox_inches=\"tight\",pad_inches=0)\n",
    "  plt.close(fig)\n",
    "  del sample_rate, samples, fig, ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 建立分类数据集\n",
    "\n",
    "1. 读取原文件\n",
    "2. 增广\n",
    "3. 7-2-1存入train-valid-test的指定类别中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/g/myWorkSpace/wave-estimate\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "HOME = os.getcwd()\n",
    "print(HOME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "def get_file_from_folder(folder, suffixes='.jpg'):\n",
    "    files = [f for f in os.listdir(folder) if f.endswith(suffixes)]\n",
    "    return files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/g/myWorkSpace/wave-estimate/dataset/free-spoken-digit-dataset-master/recordings\n",
      "/mnt/g/myWorkSpace/wave-estimate/dataset/annotationed\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "ORIGIN_IMAGE_FOLOER = os.path.join(HOME, 'dataset/free-spoken-digit-dataset-master/recordings')\n",
    "print(ORIGIN_IMAGE_FOLOER)\n",
    "OUTPUT_FOLDER = os.path.join(HOME, 'dataset/annotationed')\n",
    "print(OUTPUT_FOLDER)\n",
    "\n",
    "try:\n",
    "    os.makedirs(OUTPUT_FOLDER, exist_ok=True)\n",
    "except Exception as e:\n",
    "    print(f\"Error creating directory: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 增广音频文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 38/2411 [00:16<16:31,  2.39it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1932\n",
      "  warnings.warn(\n",
      "  2%|▏         | 44/2411 [00:18<17:14,  2.29it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2035\n",
      "  warnings.warn(\n",
      "  7%|▋         | 172/2411 [01:17<17:52,  2.09it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2016\n",
      "  warnings.warn(\n",
      "  7%|▋         | 175/2411 [01:19<18:16,  2.04it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1493\n",
      "  warnings.warn(\n",
      "  7%|▋         | 177/2411 [01:20<18:18,  2.03it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1525\n",
      "  warnings.warn(\n",
      "  7%|▋         | 178/2411 [01:20<18:13,  2.04it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1742\n",
      "  warnings.warn(\n",
      "  8%|▊         | 181/2411 [01:21<18:00,  2.06it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1866\n",
      "  warnings.warn(\n",
      "  8%|▊         | 184/2411 [01:23<17:59,  2.06it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1914\n",
      "  warnings.warn(\n",
      "  8%|▊         | 186/2411 [01:24<18:15,  2.03it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1568\n",
      "  warnings.warn(\n",
      "  8%|▊         | 187/2411 [01:24<18:32,  2.00it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1627\n",
      "  warnings.warn(\n",
      "  8%|▊         | 188/2411 [01:25<18:46,  1.97it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1772\n",
      "  warnings.warn(\n",
      "  8%|▊         | 192/2411 [01:27<18:37,  1.99it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1597\n",
      "  warnings.warn(\n",
      "  8%|▊         | 194/2411 [01:28<18:52,  1.96it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1801\n",
      "  warnings.warn(\n",
      "  9%|▊         | 206/2411 [01:34<19:01,  1.93it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1475\n",
      "  warnings.warn(\n",
      "  9%|▊         | 207/2411 [01:35<18:49,  1.95it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1852\n",
      "  warnings.warn(\n",
      "  9%|▉         | 211/2411 [01:37<19:05,  1.92it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1953\n",
      "  warnings.warn(\n",
      "  9%|▉         | 212/2411 [01:37<19:11,  1.91it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1819\n",
      "  warnings.warn(\n",
      "  9%|▉         | 213/2411 [01:38<18:35,  1.97it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1664\n",
      "  warnings.warn(\n",
      "  9%|▉         | 214/2411 [01:38<17:55,  2.04it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1962\n",
      "  warnings.warn(\n",
      "  9%|▉         | 221/2411 [01:42<17:29,  2.09it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1639\n",
      "  warnings.warn(\n",
      " 10%|▉         | 234/2411 [01:48<16:40,  2.17it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1601\n",
      "  warnings.warn(\n",
      " 10%|▉         | 239/2411 [01:50<15:44,  2.30it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1288\n",
      "  warnings.warn(\n",
      " 10%|█         | 246/2411 [01:53<15:37,  2.31it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1910\n",
      "  warnings.warn(\n",
      " 10%|█         | 249/2411 [01:54<16:08,  2.23it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1854\n",
      "  warnings.warn(\n",
      " 10%|█         | 250/2411 [01:55<17:08,  2.10it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1807\n",
      "  warnings.warn(\n",
      " 10%|█         | 253/2411 [01:56<16:04,  2.24it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1704\n",
      "  warnings.warn(\n",
      " 11%|█         | 254/2411 [01:57<15:48,  2.27it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1896\n",
      "  warnings.warn(\n",
      " 11%|█         | 255/2411 [01:57<16:25,  2.19it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2042\n",
      "  warnings.warn(\n",
      " 11%|█         | 258/2411 [01:58<17:27,  2.06it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2037\n",
      "  warnings.warn(\n",
      " 11%|█         | 260/2411 [02:00<17:56,  2.00it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1943\n",
      "  warnings.warn(\n",
      " 11%|█▏        | 272/2411 [02:05<16:52,  2.11it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2027\n",
      "  warnings.warn(\n",
      " 11%|█▏        | 273/2411 [02:06<17:16,  2.06it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1906\n",
      "  warnings.warn(\n",
      " 12%|█▏        | 279/2411 [02:09<18:10,  1.95it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1900\n",
      "  warnings.warn(\n",
      " 12%|█▏        | 301/2411 [02:20<19:10,  1.83it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2034\n",
      "  warnings.warn(\n",
      " 13%|█▎        | 303/2411 [02:21<18:24,  1.91it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1973\n",
      "  warnings.warn(\n",
      " 13%|█▎        | 324/2411 [02:32<16:26,  2.12it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1531\n",
      "  warnings.warn(\n",
      " 14%|█▍        | 344/2411 [02:41<16:00,  2.15it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1541\n",
      "  warnings.warn(\n",
      " 19%|█▉        | 465/2411 [03:45<16:58,  1.91it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1640\n",
      "  warnings.warn(\n",
      " 19%|█▉        | 466/2411 [03:46<16:26,  1.97it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1547\n",
      "  warnings.warn(\n",
      " 19%|█▉        | 469/2411 [03:47<14:12,  2.28it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1516\n",
      "  warnings.warn(\n",
      " 19%|█▉        | 470/2411 [03:47<14:03,  2.30it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1609\n",
      "  warnings.warn(\n",
      " 20%|█▉        | 472/2411 [03:48<14:54,  2.17it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1455\n",
      "  warnings.warn(\n",
      " 20%|█▉        | 476/2411 [03:50<15:16,  2.11it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1650\n",
      "  warnings.warn(\n",
      " 20%|█▉        | 477/2411 [03:51<15:16,  2.11it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1960\n",
      "  warnings.warn(\n",
      " 20%|█▉        | 478/2411 [03:51<15:16,  2.11it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1837\n",
      "  warnings.warn(\n",
      " 20%|█▉        | 481/2411 [03:53<16:28,  1.95it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1712\n",
      "  warnings.warn(\n",
      " 20%|█▉        | 482/2411 [03:53<16:29,  1.95it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1774\n",
      "  warnings.warn(\n",
      " 20%|██        | 484/2411 [03:54<15:54,  2.02it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1884\n",
      "  warnings.warn(\n",
      " 20%|██        | 489/2411 [03:57<14:59,  2.14it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1805\n",
      "  warnings.warn(\n",
      " 20%|██        | 493/2411 [03:58<14:52,  2.15it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1987\n",
      "  warnings.warn(\n",
      " 20%|██        | 494/2411 [03:59<14:24,  2.22it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1713\n",
      "  warnings.warn(\n",
      " 21%|██        | 498/2411 [04:01<15:18,  2.08it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2023\n",
      "  warnings.warn(\n",
      " 21%|██        | 510/2411 [04:06<14:48,  2.14it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1915\n",
      "  warnings.warn(\n",
      " 21%|██        | 511/2411 [04:07<14:52,  2.13it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1931\n",
      "  warnings.warn(\n",
      " 21%|██▏       | 513/2411 [04:08<14:41,  2.15it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1793\n",
      "  warnings.warn(\n",
      " 21%|██▏       | 514/2411 [04:08<14:55,  2.12it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1824\n",
      "  warnings.warn(\n",
      " 22%|██▏       | 520/2411 [04:11<15:10,  2.08it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1579\n",
      "  warnings.warn(\n",
      " 22%|██▏       | 522/2411 [04:12<15:15,  2.06it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1817\n",
      "  warnings.warn(\n",
      " 22%|██▏       | 534/2411 [04:18<15:00,  2.09it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1876\n",
      "  warnings.warn(\n",
      " 22%|██▏       | 536/2411 [04:19<13:51,  2.26it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1958\n",
      "  warnings.warn(\n",
      " 22%|██▏       | 539/2411 [04:20<13:41,  2.28it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1560\n",
      "  warnings.warn(\n",
      " 23%|██▎       | 545/2411 [04:23<14:46,  2.10it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1795\n",
      "  warnings.warn(\n",
      " 23%|██▎       | 548/2411 [04:24<15:18,  2.03it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2036\n",
      "  warnings.warn(\n",
      " 23%|██▎       | 550/2411 [04:25<15:18,  2.03it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1846\n",
      "  warnings.warn(\n",
      " 23%|██▎       | 556/2411 [04:28<15:45,  1.96it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1803\n",
      "  warnings.warn(\n",
      " 23%|██▎       | 558/2411 [04:29<15:57,  1.93it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1945\n",
      "  warnings.warn(\n",
      " 23%|██▎       | 560/2411 [04:30<16:23,  1.88it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2001\n",
      "  warnings.warn(\n",
      " 24%|██▎       | 568/2411 [04:35<16:58,  1.81it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1892\n",
      "  warnings.warn(\n",
      " 24%|██▍       | 573/2411 [04:37<16:57,  1.81it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2005\n",
      "  warnings.warn(\n",
      " 24%|██▍       | 586/2411 [04:45<16:45,  1.81it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1697\n",
      "  warnings.warn(\n",
      " 25%|██▍       | 597/2411 [04:51<16:41,  1.81it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1903\n",
      "  warnings.warn(\n",
      " 25%|██▌       | 608/2411 [04:57<16:31,  1.82it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1919\n",
      "  warnings.warn(\n",
      " 33%|███▎      | 785/2411 [06:28<12:48,  2.12it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1832\n",
      "  warnings.warn(\n",
      " 34%|███▎      | 812/2411 [06:41<13:26,  1.98it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2039\n",
      "  warnings.warn(\n",
      " 34%|███▎      | 813/2411 [06:42<13:20,  2.00it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1873\n",
      "  warnings.warn(\n",
      " 34%|███▍      | 820/2411 [06:46<15:02,  1.76it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1632\n",
      "  warnings.warn(\n",
      " 34%|███▍      | 823/2411 [06:47<14:40,  1.80it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1806\n",
      "  warnings.warn(\n",
      " 34%|███▍      | 830/2411 [06:51<14:17,  1.84it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2022\n",
      "  warnings.warn(\n",
      " 35%|███▍      | 834/2411 [06:53<14:51,  1.77it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2014\n",
      "  warnings.warn(\n",
      " 35%|███▍      | 835/2411 [06:54<14:35,  1.80it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2045\n",
      "  warnings.warn(\n",
      " 36%|███▌      | 856/2411 [07:06<13:16,  1.95it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1790\n",
      "  warnings.warn(\n",
      " 36%|███▌      | 857/2411 [07:06<13:04,  1.98it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1705\n",
      "  warnings.warn(\n",
      " 38%|███▊      | 909/2411 [07:34<13:40,  1.83it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1359\n",
      "  warnings.warn(\n",
      " 47%|████▋     | 1128/2411 [09:28<11:38,  1.84it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1673\n",
      "  warnings.warn(\n",
      " 47%|████▋     | 1136/2411 [09:32<11:44,  1.81it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1970\n",
      "  warnings.warn(\n",
      " 51%|█████     | 1222/2411 [10:19<10:53,  1.82it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1761\n",
      "  warnings.warn(\n",
      " 56%|█████▋    | 1361/2411 [11:24<09:06,  1.92it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1722\n",
      "  warnings.warn(\n",
      " 56%|█████▋    | 1362/2411 [11:24<08:54,  1.96it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1850\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 1369/2411 [11:28<09:04,  1.91it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1843\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 1371/2411 [11:29<09:20,  1.86it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1778\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 1375/2411 [11:31<09:36,  1.80it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1433\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 1377/2411 [11:32<09:31,  1.81it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1296\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 1380/2411 [11:34<09:51,  1.74it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2012\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 1381/2411 [11:35<09:41,  1.77it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1911\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 1383/2411 [11:36<09:20,  1.83it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1569\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 1389/2411 [11:39<09:25,  1.81it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1434\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 1390/2411 [11:40<09:31,  1.79it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1295\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 1391/2411 [11:40<09:33,  1.78it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1403\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 1392/2411 [11:41<09:34,  1.77it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1634\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 1393/2411 [11:41<09:39,  1.76it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1357\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 1396/2411 [11:43<09:29,  1.78it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1968\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 1398/2411 [11:44<09:10,  1.84it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1593\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 1407/2411 [11:49<09:20,  1.79it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1913\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 1408/2411 [11:49<09:07,  1.83it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1149\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 1409/2411 [11:50<09:13,  1.81it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1610\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 1410/2411 [11:51<09:17,  1.80it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1259\n",
      "  warnings.warn(\n",
      " 61%|██████    | 1462/2411 [12:19<08:06,  1.95it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1251\n",
      "  warnings.warn(\n",
      " 61%|██████    | 1463/2411 [12:20<08:15,  1.91it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1302\n",
      "  warnings.warn(\n",
      " 61%|██████    | 1468/2411 [12:23<08:19,  1.89it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1988\n",
      "  warnings.warn(\n",
      " 61%|██████    | 1470/2411 [12:24<08:24,  1.87it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1294\n",
      "  warnings.warn(\n",
      " 61%|██████    | 1472/2411 [12:25<08:18,  1.88it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1446\n",
      "  warnings.warn(\n",
      " 61%|██████    | 1473/2411 [12:25<07:57,  1.96it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1830\n",
      "  warnings.warn(\n",
      " 61%|██████    | 1474/2411 [12:26<08:18,  1.88it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1440\n",
      "  warnings.warn(\n",
      " 61%|██████    | 1475/2411 [12:26<08:07,  1.92it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1710\n",
      "  warnings.warn(\n",
      " 61%|██████    | 1476/2411 [12:27<08:04,  1.93it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1714\n",
      "  warnings.warn(\n",
      " 61%|██████▏   | 1477/2411 [12:27<08:00,  1.94it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1381\n",
      "  warnings.warn(\n",
      " 61%|██████▏   | 1478/2411 [12:28<08:00,  1.94it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1868\n",
      "  warnings.warn(\n",
      " 61%|██████▏   | 1479/2411 [12:28<08:05,  1.92it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1410\n",
      "  warnings.warn(\n",
      " 61%|██████▏   | 1480/2411 [12:29<08:01,  1.93it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1542\n",
      "  warnings.warn(\n",
      " 61%|██████▏   | 1481/2411 [12:29<07:53,  1.96it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1682\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 1484/2411 [12:31<07:51,  1.97it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1148\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 1486/2411 [12:32<07:52,  1.96it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1838\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 1487/2411 [12:33<08:05,  1.90it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1502\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 1488/2411 [12:33<08:17,  1.86it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1763\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 1495/2411 [12:37<07:51,  1.94it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1450\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 1498/2411 [12:38<07:53,  1.93it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1538\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 1499/2411 [12:39<07:52,  1.93it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1833\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 1500/2411 [12:39<07:46,  1.95it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1443\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 1501/2411 [12:40<07:47,  1.95it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2043\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 1502/2411 [12:40<07:45,  1.95it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1567\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 1503/2411 [12:41<08:07,  1.86it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1781\n",
      "  warnings.warn(\n",
      " 70%|██████▉   | 1679/2411 [14:12<05:46,  2.11it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1978\n",
      "  warnings.warn(\n",
      " 71%|███████   | 1715/2411 [14:30<05:53,  1.97it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1965\n",
      "  warnings.warn(\n",
      " 71%|███████▏  | 1723/2411 [14:34<06:06,  1.88it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2020\n",
      "  warnings.warn(\n",
      " 72%|███████▏  | 1744/2411 [14:44<05:18,  2.10it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1998\n",
      "  warnings.warn(\n",
      " 75%|███████▍  | 1807/2411 [15:15<04:52,  2.07it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2044\n",
      "  warnings.warn(\n",
      " 81%|████████▏ | 1961/2411 [16:32<04:08,  1.81it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1858\n",
      "  warnings.warn(\n",
      " 82%|████████▏ | 1973/2411 [16:38<03:36,  2.02it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1936\n",
      "  warnings.warn(\n",
      " 82%|████████▏ | 1984/2411 [16:44<03:56,  1.81it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=2015\n",
      "  warnings.warn(\n",
      " 86%|████████▌ | 2068/2411 [17:26<02:53,  1.98it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1880\n",
      "  warnings.warn(\n",
      " 86%|████████▌ | 2071/2411 [17:28<02:58,  1.90it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1963\n",
      "  warnings.warn(\n",
      " 87%|████████▋ | 2099/2411 [17:43<02:37,  1.98it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1749\n",
      "  warnings.warn(\n",
      " 87%|████████▋ | 2100/2411 [17:43<02:40,  1.94it/s]/root/miniconda3/envs/autodistill_py310/lib/python3.10/site-packages/librosa/core/spectrum.py:257: UserWarning: n_fft=2048 is too large for input signal of length=1995\n",
      "  warnings.warn(\n",
      "100%|██████████| 2411/2411 [20:28<00:00,  1.96it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "from time import sleep\n",
    "wav_files = get_file_from_folder(ORIGIN_IMAGE_FOLOER, '.wav')\n",
    "count =0\n",
    "# 增广音频文件\n",
    "for wav in tqdm(wav_files):\n",
    "    sleep(0.01)\n",
    "    # 增广并保存文件\n",
    "    audioDataTransforms(wav, ORIGIN_IMAGE_FOLOER)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 音频文件转频谱图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12055/12055 [19:46<00:00, 10.16it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "from time import sleep\n",
    "\n",
    "# 增广后的wav转image\n",
    "wav_files = get_file_from_folder(ORIGIN_IMAGE_FOLOER, '.wav')\n",
    "SPEC_PATH = os.path.join(HOME, 'dataset', 'images')\n",
    "os.makedirs(SPEC_PATH, exist_ok=True)\n",
    "\n",
    "for wav in tqdm(wav_files):\n",
    "    sleep(0.01)\n",
    "    wavesToSpecs(ORIGIN_IMAGE_FOLOER, wav, SPEC_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 频谱图按比例拷贝到数据集中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/12055 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12055/12055 [06:27<00:00, 31.08it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "from time import sleep\n",
    "import json\n",
    "import random\n",
    "import shutil\n",
    "\n",
    "# 统计所有的wav并分到train valid test 记录到json文件里保存\n",
    "classes = dict({'yweweler': list(),\n",
    "                        'george': list(),\n",
    "                        'jackson': list(),\n",
    "                        'lucas': list(),\n",
    "                        'nicolas': list(),\n",
    "                        'theo': list(),\n",
    "                        })\n",
    "spc_files = get_file_from_folder(SPEC_PATH, '.jpg')\n",
    "\n",
    "for spc in tqdm(spc_files):\n",
    "    sleep(0.01)  \n",
    "    label = spc.split('_')[1]\n",
    "    name = spc\n",
    "    classes[label].append({'name':name})\n",
    "    # 随机数 0-1，小于0.7则当前图片cp到train\n",
    "    train_coe = 0.7\n",
    "    valid_coe = 0.2\n",
    "    test_coe = 0.1\n",
    "    spc_random = random.random()\n",
    "\n",
    "    # 源文件路径\n",
    "    source_file = os.path.join(SPEC_PATH, spc)\n",
    "\n",
    "    if spc_random < train_coe:\n",
    "        # 0-0.7\n",
    "        # 目标目录路径\n",
    "        target_directory = os.path.join(OUTPUT_FOLDER, 'train', f'{label}')\n",
    "        os.makedirs(target_directory, exist_ok=True)\n",
    "        # 使用 shutil.copy() 复制文件\n",
    "        destination = os.path.join(target_directory, spc)\n",
    "        shutil.copy(source_file, destination)\n",
    "    elif (spc_random < train_coe + valid_coe):\n",
    "        # 0.7-0.9\n",
    "        # 目标目录路径\n",
    "        target_directory = os.path.join(OUTPUT_FOLDER, 'valid', f'{label}')\n",
    "        os.makedirs(target_directory, exist_ok=True)\n",
    "        # 使用 shutil.copy() 复制文件\n",
    "        shutil.copy(source_file, os.path.join(target_directory, spc))\n",
    "    else:\n",
    "        # 目标目录路径\n",
    "        target_directory = os.path.join(OUTPUT_FOLDER, 'test', f'{label}')\n",
    "        os.makedirs(target_directory, exist_ok=True)\n",
    "        # 使用 shutil.copy() 复制文件\n",
    "        shutil.copy(source_file, os.path.join(target_directory, spc))\n",
    "\n",
    "# 写入 JSON 文件\n",
    "with open(os.path.join(OUTPUT_FOLDER, 'data.json'), 'w') as json_file:\n",
    "    json.dump(classes, json_file, indent=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# wav convert to image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<contextlib.ExitStack at 0x7f03e73b1090>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import torch.backends.cudnn as cudnn\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "from PIL import Image\n",
    "from tempfile import TemporaryDirectory\n",
    "\n",
    "cudnn.benchmark = True\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "├── dataset\n",
    "│   ├── train\n",
    "|       |——class0\n",
    "|       |——class1\n",
    "│   ├── valid\n",
    "│       |——class0\n",
    "|       |——class1\n",
    "└── data.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cls_name is : ['george', 'jackson', 'lucas', 'nicolas', 'theo', 'yweweler']\n"
     ]
    }
   ],
   "source": [
    "# Data augmentation and normalization for training\n",
    "# Just normalization for validation\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.Resize(418),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'valid': transforms.Compose([\n",
    "        transforms.Resize(418),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "data_dir = f'{HOME}/dataset/annotationed/'\n",
    "\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'valid']}\n",
    "\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=16,\n",
    "                                             shuffle=True, num_workers=2)\n",
    "              for x in ['train', 'valid']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'valid']}\n",
    "class_names = image_datasets['train'].classes\n",
    "print('cls_name is : {}'.format(class_names))\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize a few images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(inp, title=None):\n",
    "    \"\"\"Display image for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dataloaders['train']))\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs)\n",
    "\n",
    "imshow(out, title=[class_names[x] for x in classes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the model\n",
    "Now, let’s write a general function to train a model. Here, we will illustrate:\n",
    "\n",
    "Scheduling the learning rate\n",
    "\n",
    "Saving the best model\n",
    "\n",
    "In the following, parameter scheduler is an LR scheduler object from torch.optim.lr_scheduler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    # Create a temporary directory to save training checkpoints\n",
    "    with TemporaryDirectory() as tempdir:\n",
    "        best_model_params_path = os.path.join(tempdir, 'best_model_params.pt')\n",
    "        print('best_model_params_path is {}'.format(best_model_params_path))\n",
    "\n",
    "        torch.save(model.state_dict(), best_model_params_path)\n",
    "        best_acc = 0.0\n",
    "\n",
    "        for epoch in range(num_epochs):\n",
    "            print(f'Epoch {epoch}/{num_epochs - 1}')\n",
    "            print('-' * 10)\n",
    "\n",
    "            # Each epoch has a training and validation phase\n",
    "            for phase in ['train', 'valid']:\n",
    "                if phase == 'train':\n",
    "                    model.train()  # Set model to training mode\n",
    "                else:\n",
    "                    model.eval()   # Set model to evaluate mode\n",
    "\n",
    "                running_loss = 0.0\n",
    "                running_corrects = 0\n",
    "\n",
    "                # Iterate over data.\n",
    "                for inputs, labels in dataloaders[phase]:\n",
    "                    inputs = inputs.to(device)\n",
    "                    labels = labels.to(device)\n",
    "\n",
    "                    # zero the parameter gradients\n",
    "                    optimizer.zero_grad()\n",
    "\n",
    "                    # forward\n",
    "                    # track history if only in train\n",
    "                    with torch.set_grad_enabled(phase == 'train'):\n",
    "                        outputs = model(inputs)\n",
    "                        _, preds = torch.max(outputs, 1)\n",
    "                        loss = criterion(outputs, labels)\n",
    "\n",
    "                        # backward + optimize only if in training phase\n",
    "                        if phase == 'train':\n",
    "                            loss.backward()\n",
    "                            optimizer.step()\n",
    "\n",
    "                    # statistics\n",
    "                    running_loss += loss.item() * inputs.size(0)\n",
    "                    running_corrects += torch.sum(preds == labels.data)\n",
    "                if phase == 'train':\n",
    "                    scheduler.step()\n",
    "\n",
    "                epoch_loss = running_loss / dataset_sizes[phase]\n",
    "                epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "                print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n",
    "\n",
    "                # deep copy the model\n",
    "                if phase == 'valid' and epoch_acc > best_acc:\n",
    "                    best_acc = epoch_acc\n",
    "                    torch.save(model.state_dict(), best_model_params_path)\n",
    "\n",
    "            print()\n",
    "\n",
    "        time_elapsed = time.time() - since\n",
    "        print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')\n",
    "        print(f'Best val Acc: {best_acc:4f}')\n",
    "\n",
    "        # load best model weights\n",
    "        model.load_state_dict(torch.load(best_model_params_path))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the model predictions\n",
    "Generic function to display predictions for a few images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_model(model, num_images=6):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['valid']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title(f'predicted: {class_names[preds[j]]}')\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finetuning the ConvNet\n",
    "\n",
    "Load a pretrained model and reset final fully connected layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /root/.cache/torch/hub/pytorch_vision_main\n",
      "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
      "100%|██████████| 97.8M/97.8M [00:07<00:00, 13.8MB/s]\n"
     ]
    }
   ],
   "source": [
    "# from a github repo\n",
    "repo = 'pytorch/vision'\n",
    "model_ft = torch.hub.load(repo, 'resnet50', weights='ResNet50_Weights.IMAGENET1K_V1')\n",
    "# model_ft = models.resnet18(weights='IMAGENET1K_V1')\n",
    "num_ftrs = model_ft.fc.in_features\n",
    "# Here the size of each output sample is set to 2.\n",
    "# Alternatively, it can be generalized to ``nn.Linear(num_ftrs, len(class_names))``.\n",
    "model_ft.fc = nn.Linear(num_ftrs, len(class_names))\n",
    "\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train and evaluate\n",
    "\n",
    "It should take around 15-25 min on CPU. On GPU though, it takes less than a minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_model_params_path is /tmp/tmpwkififcc/best_model_params.pt\n",
      "Epoch 0/24\n",
      "----------\n",
      "train Loss: 0.1727 Acc: 0.9465\n",
      "valid Loss: 0.0442 Acc: 0.9862\n",
      "\n",
      "Epoch 1/24\n",
      "----------\n",
      "train Loss: 0.0493 Acc: 0.9853\n",
      "valid Loss: 0.0215 Acc: 0.9950\n",
      "\n",
      "Epoch 2/24\n",
      "----------\n",
      "train Loss: 0.0265 Acc: 0.9934\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/mnt/g/myWorkSpace/wave-estimate/audio_class_FSDD.ipynb Cell 32\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu/mnt/g/myWorkSpace/wave-estimate/audio_class_FSDD.ipynb#X42sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m model_ft \u001b[39m=\u001b[39m train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/mnt/g/myWorkSpace/wave-estimate/audio_class_FSDD.ipynb#X42sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m                        num_epochs\u001b[39m=\u001b[39;49m\u001b[39m25\u001b[39;49m)\n",
      "\u001b[1;32m/mnt/g/myWorkSpace/wave-estimate/audio_class_FSDD.ipynb Cell 32\u001b[0m line \u001b[0;36m4\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/mnt/g/myWorkSpace/wave-estimate/audio_class_FSDD.ipynb#X42sdnNjb2RlLXJlbW90ZQ%3D%3D?line=43'>44</a>\u001b[0m             optimizer\u001b[39m.\u001b[39mstep()\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/mnt/g/myWorkSpace/wave-estimate/audio_class_FSDD.ipynb#X42sdnNjb2RlLXJlbW90ZQ%3D%3D?line=45'>46</a>\u001b[0m     \u001b[39m# statistics\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell://wsl%2Bubuntu/mnt/g/myWorkSpace/wave-estimate/audio_class_FSDD.ipynb#X42sdnNjb2RlLXJlbW90ZQ%3D%3D?line=46'>47</a>\u001b[0m     running_loss \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m loss\u001b[39m.\u001b[39;49mitem() \u001b[39m*\u001b[39m inputs\u001b[39m.\u001b[39msize(\u001b[39m0\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/mnt/g/myWorkSpace/wave-estimate/audio_class_FSDD.ipynb#X42sdnNjb2RlLXJlbW90ZQ%3D%3D?line=47'>48</a>\u001b[0m     running_corrects \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39msum(preds \u001b[39m==\u001b[39m labels\u001b[39m.\u001b[39mdata)\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/mnt/g/myWorkSpace/wave-estimate/audio_class_FSDD.ipynb#X42sdnNjb2RlLXJlbW90ZQ%3D%3D?line=48'>49</a>\u001b[0m \u001b[39mif\u001b[39;00m phase \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m'\u001b[39m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
    "                       num_epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize_model(model_ft)\n",
    "# Define the path to the directory\n",
    "directory_path = f'{HOME}/finetune/save'\n",
    "\n",
    "# Use os.makedirs to create the directory (including parent directories if they don't exist)\n",
    "os.makedirs(directory_path, exist_ok=True)\n",
    "\n",
    "# save model_ft, that is better.\n",
    "torch.save(model_ft.state_dict(), f'{HOME}/finetune/save/resnet_50.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测一张网络图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Download an Image from Bing API\n",
    "import requests\n",
    "\n",
    "from PIL import Image\n",
    "from IPython.display import display\n",
    "import requests\n",
    "from io import BytesIO\n",
    "\n",
    "# Replace 'image_url' with the actual URL of the image you want to open\n",
    "image_url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/0/08/Heavy_raining_in_Ajdov%C5%A1%C4%8Dina._%285985812613%29.jpg/1200px-Heavy_raining_in_Ajdov%C5%A1%C4%8Dina._%285985812613%29.jpg'\n",
    "\n",
    "# Set a user-agent header to comply with the User-Agent policy\n",
    "headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'}\n",
    "\n",
    "# Download the image with the user-agent header\n",
    "response = requests.get(image_url, headers=headers)\n",
    "\n",
    "# Check for HTTP errors\n",
    "response.raise_for_status()\n",
    "\n",
    "# Open the image using PIL\n",
    "img_data = BytesIO(response.content)\n",
    "\n",
    "# Open the image using PIL\n",
    "image = Image.open(img_data)\n",
    "\n",
    "# Make a prediction\n",
    "if image is not None:\n",
    "\n",
    "    # Display the image\n",
    "    display(image)\n",
    "    # Make predictions\n",
    "    # from a local directory\n",
    "    # Define the directory path and model file name\n",
    "    directory_path = '/content/save'\n",
    "    model_filename = 'resnet_50.pth'\n",
    "\n",
    "    # Create the full path to the model file\n",
    "    model_path = os.path.join(directory_path, model_filename)\n",
    "\n",
    "    # Load the model\n",
    "    model = model_ft  # Assuming the model architecture is ResNet-50\n",
    "    model.load_state_dict(torch.load(model_path))\n",
    "    model.eval()\n",
    "\n",
    "    transform = transforms.Compose([\n",
    "      transforms.Resize((418, 418)),  # 根据模型的输入大小调整\n",
    "      transforms.ToTensor(),\n",
    "      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "    ])\n",
    "    input_tensor = transform(image)\n",
    "    input_batch = input_tensor.unsqueeze(0)  # 添加一个批次维度\n",
    "    input_batch = input_batch.to(device)\n",
    "    # 使用模型进行推理\n",
    "    with torch.no_grad():\n",
    "        output = model(input_batch)\n",
    "\n",
    "    # Convert the output to probabilities using softmax\n",
    "    probabilities = torch.nn.functional.softmax(output[0], dim=0)\n",
    "\n",
    "    # Get the predicted class index\n",
    "    predicted_class_index = torch.argmax(probabilities).item()\n",
    "\n",
    "    # Print the predicted class and probability\n",
    "    predicted_class_name = class_names[predicted_class_index]\n",
    "    predicted_probability = probabilities[predicted_class_index].item()\n",
    "\n",
    "    print(f\"Predicted class: {predicted_class_name}\")\n",
    "    print(f\"Probability: {predicted_probability:.2%}\")\n",
    "\n",
    "else:\n",
    "    print(\"Image is None. Check if the image was successfully opened.\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (BETA) QUANTIZED TRANSFER LEARNING FOR COMPUTER VISION TUTORIAL\n",
    "\n",
    "You can also combine the above two methods: First you can freeze the feature extractor, and train the head. After that, you can unfreeze the feature extractor (or part of it), set the learning rate to something smaller, and continue training.\n",
    "\n",
    "In this part you will use the first method – extracting the features using a quantized model.\n",
    "\n",
    "因此，\"Quantized Transfer Learning\" 意味着在进行迁移学习的过程中，对模型进行了量化操作。这可能涉及到在源领域上使用浮点数格式的模型进行训练，然后在目标领域上对模型进行量化，以获得在嵌入式设备上更高效的推理性能。\n",
    "\n",
    "特别地，量化可以降低模型的存储需求和计算复杂度，使得模型更适合于在资源受限的环境中部署。在一些嵌入式系统、移动设备或边缘计算场景中，这种技术变得尤为重要。"
   ]
  }
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